Research Article
BibTex RIS Cite

Güneş ışınımı tahmini için CNN-LSTM modeli: Performans analizi

Year 2024, Volume: 39 Issue: 4, 2155 - 2162, 20.05.2024
https://doi.org/10.17341/gazimmfd.1243823

Abstract

Güneş enerjisinin potansiyelinden tam anlamıyla faydalanmak ve güneş enerjisi sistemlerini etkin bir şekilde işletebilmek için güneş ışınımı değerinin bilinmesi büyük önem arz etmektedir. Yüksek değişkenliğe sahip güneş radyasyonu verilerinin modellenmesi karmaşık bir problemdir ve doğrusal olmayan yöntemlere ihtiyaç vardır. Bu çalışmada, güneş ışınımı tahmini için CNN ve LSTM mimarileri kullanılarak oluşturulan hibrit bir model önerilmiştir. Önerilen modelin performansı ve uygulanabilirliği Rastgele Orman, Karar Ağaçları ve K-En Yakın Komşu gibi farklı makine öğrenmesi yöntemleriyle karşılaştırılarak incelenmiştir. Çalışmada, Afyon Kocatepe Üniversitesi yerleşkesine konumlandırılan bir piranometre ile saatlik olarak ölçülmüş güneş ışınımı değerleri kullanılmıştır. Deney sonuçları, önerilen CNN-LSTM modelinin diğer yöntemlere oranla daha başarılı sonuçlar verdiğini ortaya koymuştur.

References

  • 1. Öztürk H., Dünyada ve Türkiye'de güneş enerjisinden elektrik üretimi: güncel piyasa gelişmeleri ve beklentiler, 2022.
  • 2. Chu Y, Pedro H.T, Coimbra C.F., Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning, Solar Energy, 98, 592-603, 2013.
  • 3. Qing X, Niu Y., Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM, Energy, 148, 461-8, 2018.
  • 4. Kara A., Global solar irradiance time series prediction using long short-term memory network, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 4, 7, 2019.
  • 5. Arslan G., Bayhan B., Yaman K., Mersin/Türkiye için ölçülen global güneş ışınımının yapay sinir ağları ile tahmin edilmesi ve yaygın ışınım modelleri ile karşılaştırılması, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7, 80-96, 2019.
  • 6. Ghimire S., Deo R.C., Raj N., Mi J., Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms, Applied Energy, 253, 113541, 2019.
  • 7. Kumari P., Toshniwal D., Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting, Applied Energy, 295, 117061, 2021.
  • 8. Jalali S.M.J., Khodayar M., Ahmadian S., Shafie-Khah M., Khosravi A., Islam S.M.S, A new ensemble reinforcement learning strategy for solar irradiance forecasting using deep optimized convolutional neural network models, 2021 International Conference on Smart Energy Systems and Technologies (SEST): IEEE, 1-6, 2021.
  • 9. Haider S.A., Sajid M., Sajid H., Uddin E., Ayaz Y., Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad, Renewable Energy,198, 51-60, 2022.
  • 10. Gao B., Huang X., Shi J., Tai Y., Zhang J., Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks, Renewable Energy, 162, 1665-83, 2020.
  • 11. Acikgoz H., A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting, Applied Energy, 305, 117912, 2022.
  • 12. Sorkun M.C., Incel Ö.D., Paoli C., Time series forecasting on multivariate solar radiation data using deep learning (LSTM), Turkish Journal of Electrical Engineering and Computer Sciences, 28, 211-23, 2020.
  • 13. Erturan M.B., Merdivenci F., Optimized ARIMA-ANN hybrid model for time series analysis, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (2), 1019-1032, 2022.
  • 14. LeCun Y., Bottou L., Bengio Y., Haffner P., Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 2278-324, 1998.
  • 15. Qin L., Yu N., Zhao D., Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video, Tehnički vjesnik, 25, 528-35, 2018.
  • 16. Pascanu R., Mikolov T., Bengio Y., On the difficulty of training recurrent neural networks, International conference on machine learning: PMLR, 1310-8, 2013.
  • 17. Begüm E., İnkaya T., Long short-term memory network based deep transfer learning approach for sales forecasting, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (1), 191-202, 2024.
  • 18. Bouktif S., Fiaz A., Ouni A., Serhani M.A., Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches, Energies,11, 1636, 2018.

CNN-LSTM model for solar radiation prediction: Performance analysis

Year 2024, Volume: 39 Issue: 4, 2155 - 2162, 20.05.2024
https://doi.org/10.17341/gazimmfd.1243823

Abstract

To fully utilize the potential of solar energy and effectively operate solar energy systems, it is vital to know solar radiation values. Modeling solar radiation data with high variability is a complex problem, and nonlinear methods are needed. This study proposes a hybrid model using CNN and LSTM architectures for solar radiation prediction. The performance and applicability of the proposed model are examined by comparing it with different machine learning methods such as Random Forest, Decision Tree and K-Nearest Neighbor. The study used hourly solar radiation values measured with a pyranometer on the Afyon Kocatepe University campus. Experiment results revealed that the proposed CNN-LSTM model gave more successful results than other methods.

References

  • 1. Öztürk H., Dünyada ve Türkiye'de güneş enerjisinden elektrik üretimi: güncel piyasa gelişmeleri ve beklentiler, 2022.
  • 2. Chu Y, Pedro H.T, Coimbra C.F., Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning, Solar Energy, 98, 592-603, 2013.
  • 3. Qing X, Niu Y., Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM, Energy, 148, 461-8, 2018.
  • 4. Kara A., Global solar irradiance time series prediction using long short-term memory network, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 4, 7, 2019.
  • 5. Arslan G., Bayhan B., Yaman K., Mersin/Türkiye için ölçülen global güneş ışınımının yapay sinir ağları ile tahmin edilmesi ve yaygın ışınım modelleri ile karşılaştırılması, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7, 80-96, 2019.
  • 6. Ghimire S., Deo R.C., Raj N., Mi J., Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms, Applied Energy, 253, 113541, 2019.
  • 7. Kumari P., Toshniwal D., Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting, Applied Energy, 295, 117061, 2021.
  • 8. Jalali S.M.J., Khodayar M., Ahmadian S., Shafie-Khah M., Khosravi A., Islam S.M.S, A new ensemble reinforcement learning strategy for solar irradiance forecasting using deep optimized convolutional neural network models, 2021 International Conference on Smart Energy Systems and Technologies (SEST): IEEE, 1-6, 2021.
  • 9. Haider S.A., Sajid M., Sajid H., Uddin E., Ayaz Y., Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad, Renewable Energy,198, 51-60, 2022.
  • 10. Gao B., Huang X., Shi J., Tai Y., Zhang J., Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks, Renewable Energy, 162, 1665-83, 2020.
  • 11. Acikgoz H., A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting, Applied Energy, 305, 117912, 2022.
  • 12. Sorkun M.C., Incel Ö.D., Paoli C., Time series forecasting on multivariate solar radiation data using deep learning (LSTM), Turkish Journal of Electrical Engineering and Computer Sciences, 28, 211-23, 2020.
  • 13. Erturan M.B., Merdivenci F., Optimized ARIMA-ANN hybrid model for time series analysis, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (2), 1019-1032, 2022.
  • 14. LeCun Y., Bottou L., Bengio Y., Haffner P., Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 2278-324, 1998.
  • 15. Qin L., Yu N., Zhao D., Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video, Tehnički vjesnik, 25, 528-35, 2018.
  • 16. Pascanu R., Mikolov T., Bengio Y., On the difficulty of training recurrent neural networks, International conference on machine learning: PMLR, 1310-8, 2013.
  • 17. Begüm E., İnkaya T., Long short-term memory network based deep transfer learning approach for sales forecasting, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (1), 191-202, 2024.
  • 18. Bouktif S., Fiaz A., Ouni A., Serhani M.A., Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches, Energies,11, 1636, 2018.
There are 18 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Ardan Hüseyin Eşlik 0000-0002-3495-8490

Ozan Sen 0000-0002-9913-664X

Fatih Serttaş 0000-0003-3109-716X

Early Pub Date May 17, 2024
Publication Date May 20, 2024
Submission Date January 28, 2023
Acceptance Date November 26, 2023
Published in Issue Year 2024 Volume: 39 Issue: 4

Cite

APA Eşlik, A. H., Sen, O., & Serttaş, F. (2024). Güneş ışınımı tahmini için CNN-LSTM modeli: Performans analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(4), 2155-2162. https://doi.org/10.17341/gazimmfd.1243823
AMA Eşlik AH, Sen O, Serttaş F. Güneş ışınımı tahmini için CNN-LSTM modeli: Performans analizi. GUMMFD. May 2024;39(4):2155-2162. doi:10.17341/gazimmfd.1243823
Chicago Eşlik, Ardan Hüseyin, Ozan Sen, and Fatih Serttaş. “Güneş ışınımı Tahmini için CNN-LSTM Modeli: Performans Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39, no. 4 (May 2024): 2155-62. https://doi.org/10.17341/gazimmfd.1243823.
EndNote Eşlik AH, Sen O, Serttaş F (May 1, 2024) Güneş ışınımı tahmini için CNN-LSTM modeli: Performans analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39 4 2155–2162.
IEEE A. H. Eşlik, O. Sen, and F. Serttaş, “Güneş ışınımı tahmini için CNN-LSTM modeli: Performans analizi”, GUMMFD, vol. 39, no. 4, pp. 2155–2162, 2024, doi: 10.17341/gazimmfd.1243823.
ISNAD Eşlik, Ardan Hüseyin et al. “Güneş ışınımı Tahmini için CNN-LSTM Modeli: Performans Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39/4 (May 2024), 2155-2162. https://doi.org/10.17341/gazimmfd.1243823.
JAMA Eşlik AH, Sen O, Serttaş F. Güneş ışınımı tahmini için CNN-LSTM modeli: Performans analizi. GUMMFD. 2024;39:2155–2162.
MLA Eşlik, Ardan Hüseyin et al. “Güneş ışınımı Tahmini için CNN-LSTM Modeli: Performans Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 39, no. 4, 2024, pp. 2155-62, doi:10.17341/gazimmfd.1243823.
Vancouver Eşlik AH, Sen O, Serttaş F. Güneş ışınımı tahmini için CNN-LSTM modeli: Performans analizi. GUMMFD. 2024;39(4):2155-62.